Situation

Preparing for the upcoming holiday season, a global apparel retailer was faced with increasing challenges due to an ever expanding SKU portfolio to meet customer demand and a tightening labor market. Identifying the increased dependency on manual picking throughout its fulfillment operations, the retailer sought the power of AI combined with robotics AI powered solution and selected Kindred AI (MHI member) for their expertise in AI-powered piece-picking robots.

Action

Evaluating the labor intensive tasks related to picking, the retailer started implementation with its manual putwall stations as a prime example of where the skillfulness of human hands are required to pick and handle individual items. The retailer installed a handful of Kindred’s piece-picking robots enabled by AutoGrasp intelligence technology. AutoGrasp uses computer vision algorithms to enable the robot to assess shapes and sizes of SKUs, grasp algorithms combined with a custom gripper to ensure high degrees of picking accuracy, and motion planning algorithms to deliver a fast and efficient range of movement to place picked items safely in final destinations. The solution evaluates millions of data points to calculate and execute an optimal pick strategy for each task in real-time. This robotics intelligence platform, a concert of machine learning and reinforcement learning algorithms that play together to operate robotics in real-world production environments, was able to grow smarter with every pick, cycling through learning and executing pick strategies across the span of the retailer’s SKU portfolio.

Results

By the end of peak season, the retailer’s catalogue had grown by 70%. With the power of machine learning, the Kindred robots had successfully picked hundreds of thousands of items. AutoGrasp took little time to learn how to pick the vast assortment. As a result, the retailer was able to develop a stronger hiring plan for the following year, and installed more smart robots across its network of distribution centers.